Automatic determination and location of product support infrastructure resources

ABSTRACT

A method and system for enabling an automatic determination and allocation of product support resources. The automated process allocates support resources based on a combination of product and market requirements and historical data on resources used by similar products in similar markets. Projects requiring a support infrastructure are algorithmically classified by a combination of product complexity metrics and target market maturity metrics. Then, support infrastructure requirements are calculated based on the combined classification along with historical usage records for similarly classified products. The classification and allocation process is automated so that a product (or multiple products competing for available resources) can self-provision/request its resource requirement as a part of the product development and go-to-market process.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates generally to resource allocation andspecifically to a method and system for determining the correct amountof resources to allocate to products. Still more particularly, thepresent invention relates to a method and system for enabling autonomicdetermination of correct levels of product support resources to allocateto new/developing products.

2. Description of the Related Art

Consumers of new and existing products typically rely on the companycreating/releasing the product to provide technical and other supportfor implementing and maintaining the product. As new products, or newreleases of existing products, are brought to market, it is thereforenecessary to ensure that adequate support infrastructure (technical andotherwise) is put in place to handle consumer needs for support of theproduct. This infrastructure typically includes problemreporting/resolution, technical sales support (e.g., skills, demos,etc), education, and consulting services. Because most companies producemultiple products and or multiple versions of a single product, each ofthese support areas typically handle requests from multiple consumers ofdifferent products. The source company thus needs to be able to predictthe amount of resources that will be needed for a particular productrelease. When, as in most instances, there are limited support resourcesavailable, the company must further determine how to allocate theavailable resources across multiple products and multiple requests. Thisdetermination may require implementing a prioritization scheme forhandling incoming requests concerning multiple different products (ormultiple versions of the same product).

Existing approaches to this problem rely mainly on subjectiveassessments of the amount and type of resources that will be required tosupport a release. These existing approaches have not included empiricalmeasurements that define the environment of a release, which in turn,affects the types and amount of support resources that are actuallyneeded. Thus, the existing approaches typically results in ineffectiveallocation and/or utilization of resources, whereby some products do nothave the amount of support needed, while other products have moresupport than is required.

Some development has occurred in the area of prioritizing resourceallocation and in automating business processes. However, currentefforts to automate business processes have not yet addressed theallocation of support resources. Current resource allocation approachesdo not base allocations upon algorithmically determined product andtarget market requirements. For example, one approach utilizes theperceived or determined importance of the product to determine theamount of resources to allocate to support the product. This approachyields incorrect results because, while a product may be key (important)to a business strategy, the product may actually require less supportresources than a new (less important) product in an emerging market,particularly if the product has been in the market for some time.

Also, existing Project Management approaches attempt to allocateresources based on optimization of key business goals. However, whilethe key business goal is a factor that needs to be considered, the keybusiness goal is not sufficient by itself, as that goal fails toconsider the state of the current support infrastructure (or lack ofone) and what may be needed to provide adequate support.

Several online examples of currently available support analysisimplementations are listed below. These include world-wide web (www)sites:

-   -   “umt.com/site/index.php?page=250,” which illustrates a product        that assigns resources based on contribution to a business        strategy;    -   “solver.com/tutorial.htm,” which illustrates a linear        optimization tool, but provides no mechanism/model for how to        assign support resources;    -   “computerworld.com/managementtopicslmanagementJstory/O,10801,69129,00.html,”        which discusses the value of portfolio management and        prioritizing projects, and suggests that they be prioritized by        business strategies; and    -   “smeal.psu.edu/isbm/web/4thNewProdNuggets2001.pdf,” which        discusses the importance of selecting the right projects to work        on. This method does not address allocation of support resources        for those projects.

Also, there are several patents and/or patent submissions that describeother methods of allocating resources. For example:

-   -   U.S. Patent Application No. 2003/0033184 describes detailed        allocations of specific resources to specific tasks and includes        looking at historical data for estimates of future costs at a        detailed task level. This submission does not account for market        maturity, as it is not an important factor in the detailed, task        level assignment being addressed;    -   U.S. Pat. No. 6,675,149 describes allocating information systems        (IS) skills (i.e., programmers, architects) to a list of        projects, where the prioritization is based on company        objectives, and the requirements of each projects. The priority        that results is the one that best utilizes the IS skills to best        meet the company's objectives rather than actual support        requirements; and    -   U.S. Pat. No. 5,963,911 describes a way to optimize cost when        assigning a set of resources to a set of jobs that need to be        performed.

In addition to the above, additional development has occurred withdefining new approaches and systems to perform day-to-day businessprocesses. These systems attempt to eliminate/reduce as much of themanual efforts required by automating as much of the analysis aspossible. None of the above implementations address allocation ofsupport resources as a computation that takes into account maturity ofthe market being targeted, complexity of the product supported, andhistorical data to better estimate future resource needs andprioritizing the use of resources. Also, none of the methods provide amechanism for automated determination and assignment of supportresources.

The present invention thus recognizes that it would be desirable to havea computational process and mechanism for allocating support resourcesbased on a combination of product and market requirements and historicaldata on resources used by similar products in similar markets. Theinvention further realizes that a process that is automated so that aproduct can “self-provision” or request these resource requirements aspart of a development and go-to-market process would be a welcomedimprovement. These and other benefits are provided by the inventiondescribed herein.

SUMMARY OF THE INVENTION

Disclosed is a method and system for enabling autonomic determinationand allocation of product support resources. The automated processallocates support resources based on a combination of product and marketrequirements and historical data on resources used by similar productsin similar markets. Projects requiring a support infrastructure arealgorithmically classified by a combination of product complexitymetrics and target market maturity metrics. Then, support infrastructurerequirements are calculated based on the combined classification alongwith historical usage records for similarly classified products. Theclassification and allocation process is automated so that a product (ormultiple products competing for available resources) canself-provision/request its resource requirement as a part of the productdevelopment and go-to-market process.

The above as well as additional objectives, features, and advantages ofthe present invention will become apparent in the following detailedwritten description.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention itself, as well as a preferred mode of use, furtherobjects, and advantages thereof, will best be understood by reference tothe following detailed description of an illustrative embodiment whenread in conjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating the overall product developmentenvironment within which the calculation of resource allocation iscompleted during product development/release according to oneimplementation of the invention;

FIG. 2 is a block diagram of component parts of a computer system thatmay be utilized to complete the automated calculations for resourceallocations according to one implementation of the invention;

FIGS. 3A-3B is a flow chart of the process of completing the calculationof required support resource allocation and completing the definitionsand calculations of costs associated with support resource allocationaccording to one implementation of the invention;

FIG. 4A illustrates the two processes involved in determining weightingfactors and data sources for each classification according to oneembodiment of the invention;

FIG. 4B is a flow chart of the process of calculating and determiningmarket maturity and product complexity according to one embodiment ofthe invention;

FIG. 5A is a block diagram representation of exemplary classificationsand values evaluated during analysis/determination of the relativescores for each major factor associated with the product according toone implementation of the invention;

FIG. 5B is a scale indicating clip levels for product analysis accordingto one embodiment of the invention; and

FIGS. 6A-6B illustrates user interfaces of an application utilized toenter values of the various characteristics of the product during theevaluation of resource requirements in accordance with one embodiment ofthe present invention.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

The present invention provides a method and system for enablingautonomic determination and allocation of product support resources. Theautomated process allocates support resources based on a combination ofproduct and market requirements and historical data on resources used bysimilar products in similar markets. Projects requiring a supportinfrastructure are algorithmically classified by a combination ofproduct complexity metrics and target market maturity metrics. Then,support infrastructure requirements are calculated based on the combinedclassification along with historical usage records for similarlyclassified products. The classification and allocation process isautomated so that a product (or multiple products competing foravailable resources) can self-provision/request its resource requirementas a part of the product development and go-to-market process.

The invention provides two key concepts. The first concept involves twoprocesses: (1) algorithmically classifying projects that require asupport infrastructure by a combination of product complexity and targetmarket maturity; then, (2) calculating support infrastructurerequirements based on that combined classification along with historicalusage records for similarly classified products. The second concept isthat of automating a substantial portion of the classification andallocation processes. Implementation of the invention results in a (setof) support resource allocation(s) returned in an autonomic format(e.g., variable/value).

With reference now to the figures, and particularly FIG. 1, there isillustrated an overview of a product environment and theinter-connection between relevant components/ facilities involved in thedata collection and support/resource calculations of the presentinvention. Central to the analysis is the product 105 that isdeveloped/manufactured for release by a product developer 101 and forwhich a support/resource calculation system (SRCS) 103 is beingestablished. The product 105 may be any item/device that is utilized bya consumer 107, and for which the consumer may require support inset-up, implementation, maintenance, etc., of the product 105. Theproduct developer 101 may not actually be affiliated with the SRCS 103.Also, the product may be provided to the consumer via a Value AddedReseller (VAR), who does not provide the support resource. Also, SRCS103 may be a division/service arm of product developer 101 or a separateindependent resource group contracted to complete the product analysisand resource calculations, and provide the support resources to theconsumer 107.

Arrows within FIG. 1 illustrate the directional flow of informationconcerning the product and other data required in the calculation by theSRCS 103. For example, actual product information (e.g., productcomplexity) is provided to the SRCS 103 by the product developer and byinternal testing/evaluation of the product. Marketing factors 106 andconsumer variables (inquiries, request for support,responses/comments/ratings, use factors, etc.) are also provided to theSRCS 103. Notably, the SRCS 103 also communicates with the consumer toeither request information or provide resources (to the consumer) whenrequested. Finally, historical data is received by the SRCS 103 from adatabase 109, which maintains historical data 111 of similar products.The SCRS 103 determines an amount of marketing that is required for theproduct's support services and generates a marketing output (i.e.,marketing component 106). The marketing component 106 also factors intothe calculations of required product support, since the amount ofmarketing determines whether the consumer is aware of the availabilityof support resources and should correlate to the level of use of theseresources by the consumer.

According to one embodiment, the SRCS 103 includes support personnel aswell as a computer system and/or other equipment required forinterfacing with the other parts of the overall product environment. Thecomputer equipment includes a data processing system that enables theautomation of several of the data collection, resource evaluation, andother features to determine a best allocation of supportresources/services.

Turning now to FIG. 2, there is illustrated an example data processingsystem (DPS) 200 that may be utilized to complete several of theautomated processes of the invention. DPS 200 includes a centralprocessing unit (CPU) 201 which is connected to memory 203 andinput/output (I/O) channel controller (CC) via Interconnect 205. Withinmemory are software components, including operating system (OS) 204 andresource allocation utility 206. Resource allocation utility (RAU) 206includes the software code that, when executed on CPU 201, provides thevarious autonomic functionality of the invention as described below.

I/O CC 207 operates as a controller for output device, such as monitor213, and various input devices, such as keyboard 209 and mouse 211.These devices are utilized to enable user-input of required data orparameters as well as user interaction with the functional (interface)features of RAU 206. In one implementation, RAU 206 provides a graphicaluser interface (GUI) displayed on monitor 213. GUI may depict anapplication for inputting classification data used in the complexitycalculations, as shown by FIG. 5A.

In addition to the aforementioned input devices, the present inventionprovides another input mechanism, referred to as product tracker 215,that completes one or more of the following functions: (1) receivestatus message from the product via a network link (such as theInternet); (2) receives consumer reporting on product status; (3)monitor electronic submissions of consumer request for support; etc. Thespecific processes of the invention are illustrated by the followingflow charts, beginning with FIGS. 3A-3B.

FIGS. 3A-3B illustrates the process for determining an amount ofinfrastructure resource to allocate to a new or upcoming product. Theprocess is completed by the resource group (SRCS 105 of FIG. 1) duringthe actual development of and/or prior to the release of the product(s).The process begins at step 301 at which a product that will require(post-release) support resources is identified (i.e., certaincharacteristics of the product made know to the personnel managing theevaluation process). Each product has different characteristics and thusrequires a different amount/level of support based on its uniqueevaluation. Thus, identification of the product is crucial todetermining the correct historical data to access, the answers given topertinent questions when calculating the clip levels, and ultimately theamount and/or level of support required. The evaluation is completedindependent of whether the calculations are completed manually or via anautomated system. Implementation of the invention, however, does notassume that the product being evaluated actually requires supportresources. Even those products that will require very little or nosupport may be analyzed in order to reach a more current and accurateconclusion about whether support is required.

Once the product is identified, the product's complexity classificationis defined at step 303. Then, the market maturity classification of theproduct is defined at step 305. Notably, both of these steps also entaildefining the characteristics that will be used to assess eachclassification, which involves the two steps that are illustrated by theflow chart of FIG. 4A. These processes include first defining aweighting factor for each complexity classification (as indicated atstep 402) and then determining data sources to be evaluated for eachclassification, as shown at step 404. These definitions may be providedvia entry into the GUI (e.g., FIGS. 5A, 6A-6B) of the RAU.

As shown by GUI 500 of FIG. 5A, each characteristic 501 is assigned atrue/false value 503 that indicates either true or false for aclassification. For purposes of the invention, each characteristic thatis “true” is assigned a value of 1 and those that are “false” areassigned a value of 0. Entry or selection of the true/false value 503occurs via the GUI provided by the RAU.

Returning to FIG. 3A, once the product complexity level and marketmaturity levels have been defined, the clip levels for both productcomplexity and market maturity are defined at step 307. The clip levelsare relative cut-off points along a scale utilized to illustraterelative rating of a product against a normalized set of gradedcharacteristics. An exemplary scale is illustrated by FIG. 5B with threeclip levels.

For simplicity of the invention, only three clip levels are defined,separating product scores into three categories: (A) simple; (B)moderate/normal; and (C) complex. Thus, for example, a product may beevaluated on a point scale of 1-20 (scale selected by SRCS and includedwithin RAU to be utilized for every product being analyzed). Eachproduct's score is then normalized to within that scale based on thenumber of criteria utilized in the evaluation. If a characteristic isattributable to the product, then the characteristic is assigned a pointwithin the GUI.

In order to determine when a product falls within A, B, or Ccategory/classification, clip levels are assigned as the cut-off points,which define a set of pre-determined ranges for a particular productclassification. Thus, in the illustrative embodiment of FIG. 5B, thescale is assigned 7 as a first clip level and 14 as a next clip level,where products rating/scoring between 1-7 are classified as “simple”products, products scoring between 8-14 are classified as “moderate”products, and products scoring above 14 classified as “complex”products. A list of characteristics, which may include some or all ofthe characteristics illustrated within the spreadsheet of FIGS. 6A-6B,is evaluated for the product. The invention contemplates an even moreexpansive list of criteria, and is therefore not limited to thoseillustrated by FIG. 5A or FIGS. 6A-6B.

In the illustrative embodiment, calculating the product classificationinvolves inputting the numbers or scores and comparing the score againstthe clip levels to determine what category the product falls into, fromamong (1) simple; (2) moderate; and (3) complex projects. Forstandardization, all product scores are made to fit within the scale.The illustrative embodiment assumes the higher up the scale a productrates, the more resources will be required. Utilizing these criteria anda standardize set of clip levels for all products, different productsmay be compare against each other by simply calculating their score(normalized to the scale, if required) and comparing their relativerating within the standardize scale.

With the clip levels determined at step 307 (of FIG. 3A), the complexityand/or market maturity classifications are assigned to the new productor new/upcoming releases, as shown at step 309. This assignment entailsa number of steps illustrated by the flow chart of FIG. 4B. In FIG. 4B,the process begins at step 413 with an examination of the data availablefrom the product and an analysis of the market to determine product andmarket characteristics.

As shown by GUIs 600 of FIGS. 6A and 6B, each characteristic 601 isassigned a true/false value 603 that indicates either true or false fora classification (similar to FIG. 5A). FIGS. 6A and 6B further indicatea weight factor assigned to each of the three GUIs associated with eachproduct. Additionally, a complexity result (e.g., MAJOR) is displayed atthe bottom of the third GUI based on the responses entered by the userfor each characteristic.

Once the characteristics have been assigned numerical values, a check ismade at step 415 whether the same characteristics are utilized for allclassifications. When the same characteristics are used for allclassifications, the product complexity score is calculated as simplythe average of the weighted score, as shown at step 417. However, whendifferent characteristics are used for different classifications thiscomplexity score is determined at step 419 as the weighted sums of thepercentage match of each classification, i.e., prodcpx=SUM(prodclaswght(i)*prodclasspct (i)).

As utilized herein, product complexity indicates how much support teammanpower is (or will be) required. Also, complexity is determined bylooking at other products, even products in another area, but comparewith similar scores for complexity level and market maturity). A productmay have a different rating based on different classifications having aset of different characteristics from another. For example, the productmay evaluate as complex in the area of demonstrations (demos), but minorin the area of education.

As illustrated within the GUI 600, information utilized to determine theproduct complexity includes items such as: (1) the release number of theproduct; (2) the amount of change being made in the release; (3) thelength of the development cycle; and (4) the number of differentdevelopment and support organizations involved. In one implementation,existing databases and websites are referenced for this type ofinformation.

Following (or concurrent with) the calculation of the product complexityin FIG. 4B, a determination is made at step 421 whether the samecharacteristics are used for each classification. As with productcomplexity, when the same characteristics are used for allclassifications, the market maturity score is simply the average of theweighted score, as shown at step 425. However, when differentcharacteristics are used for different classifications, the weightedscore is calculated at step 421, as the maximum (or best fit) of thepercentage match of each classification, i.e., mktmurty=MAX(mktmurtywght(i)*mktmurtypct (i)).

The information used to determine the market maturity includes itemssuch as: (1) the revenue growth (decline) rate; (2) education classes inplace on the product, or updates to existing classes, or number of newclasses required; and (3) existence/number of product demos. Again, someof this information is currently available in various systems.

With these calculated values (i.e., product complexity score and marketmaturity score), the actual product complexity and market maturity aredetermined according to the defined clip levels (brackets) for each, asshown at step 427. Gathering the above information allows the system tocalculate a classification for the project. The classification scheme issufficiently straightforward so that classification may also be done onproducts that have been previously released. Also, by continuallytracking and weighing resources that have been used by similarlyclassified projects, an ongoing allocation for what resources will berequired for similar projects in progress is maintained.

Returning now to FIG. 3A, and step 311, historical cost information isgathered for existing products that are either similar to (or scoresimilarly to) the new product. The historical cost information may begathered for a wide range of existing products. For each of the previousproducts, the product complexity and market maturity are calculatedsimilarly to the new product. Then, the historical cost for each type ofsupport is determined at step 313. This historical cost is based on anaverage of the historical data for the various classifications. Thecalculation provides the typical historical costs associated with acomplexity/market maturity classification.

Following, at step 315, the resource allocations for new products arecomputed based on complexity/maturity classification. To ensure theresource allocation is up-to-date, a time period is established duringwhich the calculation is assumed to be fresh/valid. When the periodexpires, the calculated resource allocation is assumed to be stale(i.e., requires a new calculation). Thus, a determination is then madeat step 317 whether the pre-established period has elapsed since thelast computation of resource allocations for the new product. When theperiod has elapsed, the historical costs are re-assessed, and theallocations being utilized are updated at step 319. This involvesperiodically updating the averages for each classification to provideallocations based on information in the historical databases.

Thus, even after the product goes to market, an ongoing review isimplemented to ensure the proper, continuing use and/or allocation ofsupport resources. In one implementation, specific metrics are utilizedto perform this ongoing review. For example, tracking the delay timebetween a problem submission and resolution indicates whether resourceswere in place to deal with the request. As a more specific example,tracking students (who take and/or attend a class) per educationinvestment indicates whether demand is in line with expectations.Variances in these types of metrics from the norm trigger investigationand possible intervention (re-calculation).

Additionally, at step 321, the product and support volumes and costs areperiodically examined to ensure the continued accuracy of allocations. Adetermination is made at step 323 whether the resource allocations areaccurate and current, and when they are not, a re-calculation (orre-evaluation) of resource allocation is performed. The resourceallocations are then utilized to plan support requirements for theupcoming and/or future product releases, as indicated at block 325. Inplanning for resource requirements for the next budget period,classifying projected products and use of historical resource usagenumbers allows early calculation of expected budget requirements. Thus,following a determination at step 327 of support budget requirementsduring the product planning phase, historical costs and classificationof projected products are utilized, at step 329, to build budgetrequirements for the next planning period.

Notably, while the invention is described from the perspective of asingle product or single product release, the invention is applicable toa pre-lease analysis for multiple products during an upcoming releaseperiod. That is, the features of the invention may be applied to a fallplan type of budgeting in which the SRCS obtains a list ofproducts/releases targeted for the next year, and after classifyingthese products and their product markets, build the expected next-yearbudget requirement for the entire product portfolio.

As a final matter, it is important that while an illustrative embodimentof the present invention has been, and will continue to be, described inthe context of a fully functional data processing system, those skilledin the art will appreciate that the software aspects of an illustrativeembodiment of the present invention are capable of being distributed asa program product in a variety of forms, and that an illustrativeembodiment of the present invention applies equally regardless of theparticular type of signal bearing media used to actually carry out thedistribution. Examples of signal bearing media include recordable typemedia such as floppy disks, hard disk drives, CD ROMs, and transmissiontype media such as digital and analog communication links.

While the invention has been particularly shown and described withreference to a preferred embodiment, it will be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention.

1. A computer-implemented method for automatically determiningpost-release resource allocation for a consumer product, the methodcomprising a computer system performing the functions of: calculatingproduct complexity of the consumer product utilizing a plurality ofproduct characteristics, including release number of the product, amountof change being made in the release, length of development cycle, andnumber of different development and support organizations involved;evaluating target market maturity of the consumer product utilizing aplurality of market characteristics, including revenue growth or declinerate, and at least one of: (a) educational classes in place, (b) anumber of new classes, and (c) updates to existing classes required, andnumber of product demonstrations; analyzing historical usage of similarproducts to the consumer product; the computer system performing anautomatic determination of a best allocation of post-release productsupport resources for the consumer product from a combination of thecalculated product complexity, the target market maturity, thehistorical usage, and a consumer awareness of the post-release productsupport resources, wherein the best allocation of post-release productsupport resources correlates to a level of post-release product supportresources utilized by consumers, and wherein the post-release productsupport resources include support for one or more of set-up,implementation, and maintenance of the consumer product; providing thebest allocation of post-release product support resources to theconsumers; wherein the calculating step further comprises: identifyingthe product; determining a list of characteristics for use in assessingclassifications of the product complexity and market maturity, thedetermining further comprising: defining a weighting factor for eachcomplexity classification; and determining data sources to be evaluatedfor each classification; assigning a first value to each characteristicattributable to the product and a second value to each characteristicnot attributable to the product; and evaluating a relative rating of theproduct against a normalized rating chart based on a calculationinvolving at least one of the first value and the second value assignedto each characteristic, wherein evaluating the relative rating of theproduct based on the calculation further comprises: determining whethereach classification among multiple classifications for a product utilizea same set of characteristics; in response to the same set ofcharacteristics being utilized for each classification, calculating botha product complexity score and a market maturity score as an average ofa weighted score; and in response to different characteristics beingused for different classifications, calculating the product complexityscore as weighted sums of a percentage match of each classification, andcalculating the market maturity score as a maximum of the percentagematch of each classification.
 2. The method of claim 1, performing theautomatic determination further comprises: importing data about thehistorical usage from a database of historical usage; receiving a seriesof responses to questions about characteristics of the product and thetarget market; evaluating the responses; and determining the bestallocation of post-release product support resources for the consumerproduct utilizing the evaluation of the responses and the historicalusage data.
 3. The method of claim 1 further comprising outputting viaan output device a result of the automatic determination, wherein theoutput represents a resource allocation that is implemented for theproduct when the product is released, the resource allocation includinga number of personnel, an associated cost, and a level of marketingrequired for the product, and wherein the resource allocation isself-provisioned by a product developer within a development andgo-to-market process for the product; and wherein the post-releaseproduct support resources include one or more of: support personnel;computer systems; and equipment required for interfacing with portionsof the product.
 4. The method of claim 1, further comprising: receivingone or more updates to product characteristics and market maturity froman external source; and in response to receiving updates to dataassociated with the product characteristics and the market maturitypost-release of the product, initiating a re-analysis of the product foran updated resource allocation calculation, wherein the resourceallocation is periodically updated based on real-time application of theproduct in a customer environment.
 5. The method of claim 1, theidentifying the product further comprising: first assigning clip levelsindicating a break in the normalized rating chart at which a productevaluates as having a specific classification among multipleclassifications delineated by the clip levels.
 6. The method of claim 1,wherein the performing an automatic determination further comprises: inresponse to multiple products being developed for later release,providing the autonomic determination of best allocation of post-releaseproduct support resources for each of the multiple products based onrelative metrics calculated for each of the multiple products given atotal amount of support resources available for allocating to acombination of the multiple products, wherein the performing stepenables early preparation of an expected next-year budget requirementfor an entire product portfolio.
 7. A system for automaticallydetermining resource allocation for a consumer product, the systemcomprising: a processor configured to: algorithmically classify consumerproducts that requiring a support infrastructure based on a combinationof determined product complexity and target market maturity for each ofthe consumer products by calculating product complexity of each of theconsumer products utilizing a plurality of product characteristics,including release number of the consumer products, amount of changebeing made in the release, length of development cycle, and number ofdifferent development and support organizations involved; determinesupport resource allocations based on the combined classification alongwith historical usage records for similarly classified products for eachof the consumer products; return the support resource allocations foreach of the consumer products as one or more of a series of variablesand associated values; perform an automatic determination of a bestallocation of post-release product support resources for the consumerproduct from a combination of a calculated product complexity, thetarget market maturity, a historical usage, and a consumer awareness ofthe post-release product support resources, wherein the best allocationof post-release product support resources correlates to a level ofpost-release product support resources utilized by consumers, andwherein the post-release product support resources include support forone or more of set-up, implementation, and maintenance of the consumerproduct; and provide the best allocation of post-release product supportresources to the consumers; wherein the calculating product complexityfurther comprises: identifying each of the consumer products includingfirst assigning clip levels indicating a break in a normalized ratingchart at which a product evaluates as having a specific classificationamong multiple classifications delineated by the clip levels;determining a list of characteristics for use in assessingclassifications of the product complexity and market maturity, thedetermining including: defining a weighting factor for each complexityclassification; and determining data sources to be evaluated for eachclassification; assigning a first value to each characteristicattributable to each of the consumer products and a second value to eachcharacteristic not attributable to each of the consumer products; andevaluating a relative rating of each of the consumer products against anormalized rating chart based on a calculation involving at least one ofthe first value and the second value assigned to each characteristic,wherein evaluating based on the calculation comprises: determiningwhether each classification among multiple classifications for a productutilize a same set of characteristics; in response to the same set ofcharacteristics being utilized for each classification, calculating botha product complexity score and a market maturity score as an average ofa weighted score; and in response to different characteristics beingused for different classifications, calculating the product complexityscore as weighted sums of a percentage match of each classification, andcalculating the market maturity score as a maximum of the percentagematch of each classification.
 8. The system of claim 7 wherein: thealgorithmically classify consumer products further comprises: evaluatingtarget market maturity of each of the consumer products utilizing aplurality of market characteristics, including revenue growth or declinerate, and, at least one of: (a) educational classes in place, (b) anumber of new classes, and (c) updates to existing classes required, andnumber of product demonstrations; the determine support resourceallocations further comprises analyzing historical usage of similarproducts to each of the consumer products; and the perform an automaticdetermination further comprises: importing data about the historicalusage from a database of historical usage; and receiving a series ofresponses to questions about characteristics of the consumer productsand the target market; evaluating the responses; and utilizing theevaluation of the responses and the historical usage data to determinethe best allocation of post-release product support resources for theconsumer product.
 9. The system of claim 7, wherein the processor isfurther configured to: output a result of the automatic determination,wherein the output represents a resource allocation that is implementedfor the consumer products when the consumer products are released, theresource allocation including a number of personnel, an associated cost,and a level of marketing required for the consumer products, and whereinthe resource allocation is self-provisioned by a product developerwithin a development and go-to-market process for the consumer products;and wherein the post-release product support resources include one ormore of: support personnel; computer systems; and equipment required forinterfacing with portions of the product.
 10. The system of claim 7,wherein the processor is further configured to: receive one or moreupdates to product characteristics and market maturity from an externalsource; and in response to receiving updates to data associated with theproduct characteristics and the market maturity post-release of theconsumer products, extend a re-analysis of the consumer products for anupdated resource allocation calculation, wherein the resource allocationis periodically updated based on real-time application of the consumerproducts in a customer environment.
 11. A computer program productcomprising: a computer readable storage medium; and instructions on thecomputer readable storage medium that execute on a processor of acomputer device to enable automatic determination of resource allocationfor a consumer product, the instructions including instructions for:algorithmically classifying consumer products that require a supportinfrastructure based on a combination of determined product complexityand target market maturity for each of the consumer products bycalculating product complexity utilizing a plurality of productcharacteristics, including release number of the product, amount ofchange being made in the release, length of development cycle, andnumber of different development and support organizations involved; anddetermining support resource allocations based on the combinedclassification along with historical usage records for similarlyclassified products for each of the consumer products; returning thesupport resource allocations for each of the consumer products as one ormore of a series of variables and associated values; performing anautomatic determination of a best allocation of post-release productsupport resources for the consumer product from a combination of acalculated product complexity, the target market maturity, thehistorical usage, and a consumer awareness of the post-release productsupport resources, wherein the best allocation of post-release productsupport resources correlates to a level of post-release product supportresources utilized by consumers, and wherein the post-release productsupport resources include support for one or more of set-up,implementation, and maintenance of the consumer product; and providing abest allocation of post-release product support resources to consumersof each consumer product wherein the instructions for calculatingfurther comprises instructions for: first assigning clip levelsindicating a break in the normalized rating chart at which a productevaluates as having a specific classification among multipleclassifications delineated by the clip levels; identifying the product;determining a list of characteristics for use in assessingclassifications of the product complexity and market maturity; defininga weighting factor for each complexity classification; determining datasources to be evaluated for each classification; assigning a first valueto each characteristic attributable to the product and a second value toeach characteristic not attributable to the product; and evaluating arelative rating of the product against a normalized rating chart basedon a calculation involving at least one of the first value and thesecond value assigned to each characteristic, wherein the instructionsfor evaluating based on the calculation further comprises instructionsfor: determining whether each classification among multipleclassifications for a product utilize a same set of characteristics; inresponse to the same set of characteristics being utilized for eachclassification, calculating both a product complexity score and a marketmaturity score as an average of a weighted score; and in response todifferent characteristics being used for different classifications,calculating the product complexity score as weighted sums of apercentage match of each classification, and calculating the marketmaturity score as a maximum of the percentage match of eachclassification.
 12. The computer program product of claim 11, furthercomprising instructions for: providing a graphical user interface (GUI)that includes a display of product characteristics for eachclassification; receiving, via the GUI, user responses to each of theproduct characteristics; calculating the product complexity and marketmaturity from the user responses; and outputting on an output device aresult of the automatic determination, wherein the output represents aresource allocation that is implemented for each of the consumerproducts when each of the consumer products is released, the resourceallocation including a number of personnel, an associated cost, and alevel of marketing required for each of the consumer products, andwherein the resource allocation is self-provisioned by a productdeveloper within a development and go-to-market process for each of theconsumer products; and in response to multiple products being developedfor later release, providing the automatic determination of bestallocation of post-release product support resources for each of themultiple products based on relative metrics calculated for each of themultiple products given a total amount of support resources availablefor allocating to a combination of the multiple products, wherein theperforming—enables early preparation of an expected next-year budgetrequirement for an entire product portfolio; wherein the post-releaseproduct support resources include one or more of: support personnel;computer systems; and equipment required for interfacing with portionsof the product.
 13. The computer program product of claim 11 wherein:the instructions for algorithmically classifying consumer productsfurther comprises instructions for: evaluating target market maturityutilizing a plurality of market characteristics, including revenuegrowth or decline rate, educational classes in place or number of newclasses or updates to existing classes required, and number of productdemonstrations; and the instructions for determining support resourceallocations comprises instructions for analyzing historical usage ofsimilar products.
 14. The computer program product of claim 13, whereinthe instructions for automatic determination further comprisesinstructions for: importing data about the historical usage from adatabase of historical usage; receiving a series of responses toquestions about characteristics of each of the consumer products and thetarget market; evaluating the responses; utilizing the evaluation of theresponses and the historical usage data to determine the best allocationof post-release product support resources for the consumer products;receiving one or more updates to product characteristics and marketmaturity from an external source; and in response to receiving updatesto data associated with the product characteristics and the marketmaturity post-release of the consumer products, initiating a re-analysisof the consumer products for an updated resource allocation calculation,wherein the resource allocation is periodically updated based onreal-time application of the consumer products in a customerenvironment.